Air Quality Monitoring Technology and Its Benefits for Public Health in Indonesia - Ni Ketut Susilawati; Nurghany; Tiara Wirdadinaka; Zhafira Syarlianti Azzahra (Journal of Epidemiology and Health Science, 2026)
Background : Air pollution is a global health threat, triggering respiratory and cardiovascular diseases, and even premature death. Urbanization, the increasing number of motorized vehicles, and industrial activity exacerbate pollution in densely populated areas. This situation demands an accurate, rapid, and accessible real-time air quality monitoring system to support public health prevention and protection. Objective: This study aims to review various air quality monitoring technologies that have been developed, and to analyze the benefits of their application in improving public health. Methods : A Systematic Literature Review (SLR) was conducted by collecting and analyzing articles from Google Scholar, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Articles published between 2020 and 2025 were reviewed using the keywords "air quality," "air pollution," "Internet of Things," "public health," and "monitoring". Results: Literature shows that Internet of Things (IoT)-based monitoring technology is capable of detecting pollutants such as PM2.5, CO, NO₂, SO₂, and O₃ in real time with high accuracy. This system has early warning, is integrated with an Android or web application, and has a sensor accuracy of over 90%. Furthermore, the use of drones and machine learning algorithms has shown promising results in estimating air quality index and supporting public health programs. Conclusion: IoT-based air quality monitoring technology has proven effective, accurate, and accessible, making it useful in preventing the impact of air pollution on public health. Future developments will focus on improving sensor accuracy, utilizing 5G networks, and integrating cloud-based data for a more comprehensive monitoring system. Keywords: Environmental Pollutants, Air Pollution, Internet of Things, Public Health, Technology, Machine Learning